Signal processing mainly concerns operations on or analysis of discrete or continuous signals. Signals are analog or digital electrical representations of time or spatial varying physical quantities, such as visual and audible signals. Data rebinning or down-sampling is a fundamental operation in digital signal processing as it is useful in reducing the amount of data while not necessarily losing all of the information required for a complete signal reconstruction. Data rebinning may be used for data compression, image processing, filter design, and anti-aliasing techniques.
The process of data rebinning can be computationally intensive, require substantial run-time even with modern computers. There are two known methods of rebinning of signal data. Taking image data as an example, the first method is the pixel by pixel down-sampling technique where the value for each pixel in the down-sampled image is calculated in stages, by averaging data from a certain range of rows and columns in the original image; the second method is the matrix down-sampling technique where the initial image is transformed into the down-sampled image with two matrix multiplications, one for down-sampling the rows and the other for down-sampling the columns.
These two methods both can be computationally intensive, in other words, require substantial CPU cycles, thus runtime. Moreover, the matrix down-sampling method can require substantial computer memory to store the matrices used to perform the down-sampling. As a result of these computationally inefficient approaches, there is a need to advance the art in this field in order to more efficiently implement data rebinning, at least in terms of runtime and memory usage.
Accordingly, it would be desirable to provide a data rebinning system and method that addresses at least some of the problems identified above.